Encrypted Data Statistics via Homomorphic Encryption
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Solution Overview
Problem
In the big data era, enterprises face challenges in performing data statistics calculations across different entities without compromising data privacy, due to competitive relationships and concerns about user privacy protection, leading to a lack of effective solutions for secure and private data sharing.
Innovation Solution
A data statistics method and apparatus that enables secure calculations between two parties by using homomorphic encryption and key exchange protocols to perform statistical processing on encrypted data, ensuring that only encrypted statistical values are shared, and local decryption is performed to obtain the final statistical values, thus protecting data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If enterprises share data for statistical calculations, then data statistics capability is improved, but data privacy security deteriorates
Solution Approach 1:
The statistical data party performs homomorphic encryption on the first data before transmission, and the cooperative data party performs counterpart private key processing on data identifiers before statistical calculation. These preliminary cryptographic transformations enable subsequent statistical operations while maintaining data privacy throughout the process.
Solution Approach 2:
Homomorphic encryption serves as an intermediary mechanism that allows statistical calculations to be performed on encrypted data. The encrypted statistical values act as intermediaries that can be computed without decrypting the underlying sensitive data, resolving the contradiction between computation and privacy protection.
2Measurement precision
If enterprises perform data statistics calculations together, then statistical accuracy is improved, but data security deteriorates
Solution Approach 1:
The system performs preliminary homomorphic encryption and key exchange protocol operations before statistical calculations. This allows accurate statistical computations to be performed on encrypted data, ensuring both statistical accuracy and data security are maintained throughout the process.
Solution Approach 2:
The patent transforms data from plaintext to encrypted form using homomorphic encryption, changing the parameter state of the data. This transformation enables statistical operations to be performed on the encrypted parameter representation while maintaining the mathematical properties needed for accurate calculation.
3Productivity
If enterprises cooperate on data statistics, then data processing efficiency is improved, but privacy protection capability deteriorates
Solution Approach 1:
The cooperative data party performs counterpart private key processing and stores correlations between processed identifiers and encrypted data in advance. This preliminary preparation enables efficient statistical calculations to be performed later without repeatedly exposing or decrypting sensitive data, maintaining both efficiency and privacy.
Solution Approach 2:
The system uses encrypted statistical values as intermediaries that enable efficient collaborative computation while protecting privacy. These intermediaries allow the cooperative data party to perform statistical operations without accessing the underlying sensitive data, resolving the efficiency-privacy contradiction.
Data Source
AI summary
A data statistics method and an apparatus thereof, the method comprises: receiving, by a first processor of the cooperative data party, data identifiers corresponding to pieces of first data for the data statistics and corresponding encrypted data from the statistical data party; determining, by the first processor, an identifier intersection according to data identifiers corresponding to pieces of second data of the cooperative data party and the received data identifiers corresponding to the pieces of first data; performing, by the first processor, statistical processing on encrypted data corresponding to common data identifiers in the identifier intersection to obtain encrypted statistical values; and sending, by the first processor, the encrypted statistical values to a second processor of the statistical data party to enable the second processor to perform decryption on the encrypted statistical values and obtain the statistical values.